Lead Software Engineer - Gen AI, LLM, Agentic AI

Overview

Lead Software Engineer role focused on Generative AI, LLM and Agentic AI, leading complex, companywide technology initiatives and setting engineering standards and best practices.

What you'll do

• Lead complex technology initiatives, including those that are companywide with broad impact.
• Act as a key participant in developing standards and companywide best practices for engineering complex, large-scale technology solutions.
• Design, code, test, debug, and document for projects and programs.
• Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, the enterprise technological environment, and technical challenges requiring in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors.
• Make decisions in developing standard and companywide best practices for engineering and technology solutions, requiring understanding of industry best practices and new technologies, influencing and leading the technology team to meet deliverables and drive new initiatives.
• Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals.
• Lead projects, teams, or serve as a peer mentor.

What you'll need

• 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• Deep expertise across Generative AI, Foundation Models, RAG, Agentic AI, and multi-agent systems.
• Hands-on experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent GenAI frameworks.
• Strong knowledge of prompt engineering, context management, fine-tuning, vector databases, and AI orchestration.
• Experience with LLMOps/MLOps, AI observability, model evaluation, and benchmarking.

Nice to have

• Proven experience building scalable enterprise AI/GenAI platforms across multiple use cases.
• Experience defining GenAI standards, governance models, and reference architectures.
• Strong expertise in Responsible AI, AI governance, risk, compliance, guardrails, and model validation.
• Experience with AI red teaming, adversarial testing, prompt-injection defenses, and AI security.
• Knowledge of OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.
• Strong cloud-native architecture experience across Azure, AWS, or Google Cloud.
• Experience with Kubernetes, containers, microservices, APIs, CI/CD, Infrastructure as Code, and platform engineering.
• Demonstrated technical leadership in solution design, deployment, scalability, security, and operational excellence.
• Open-source contributions, AI research, patents, or industry thought leadership are desirable.